ParamILS: An Automatic Algorithm Configuration Framework

TLDR

Identifying performance‑optimizing parameter settings is crucial for algorithm development and application, yet default settings are often manually chosen with considerable effort. The paper proposes an automatic framework to solve the algorithm configuration problem. The framework optimizes target algorithm performance by varying ordinal or categorical parameters, reviews local‑search configuration procedures, and introduces adaptive time‑limiting techniques to accelerate evaluation. Experimental evaluation on SAT solvers and CPLEX demonstrates that the automated configuration procedures yield substantial and consistent performance improvements.

Abstract

The identification of performance-optimizing parameter settings is an important part of the development and application of algorithms. We describe an automatic framework for this algorithm configuration problem. More formally, we provide methods for optimizing a target algorithm’s performance on a given class of problem instances by varying a set of ordinal and/or categorical parameters. We review a family of local-search-based algorithm configuration procedures and present novel techniques for accelerating them by adaptively limiting the time spent for evaluating individual configurations. We describe the results of a comprehensive experimental evaluation of our methods, based on the configuration of prominent complete and incomplete algorithms for SAT. We also present what is, to our knowledge, the first published work on automatically configuring the CPLEX mixed integer programming solver. All the algorithms we considered had default parameter settings that were manually identified with considerable effort. Nevertheless, using our automated algorithm configuration procedures, we achieved substantial and consistent performance improvements.